


"""simple docstring"""



from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class    __a						( __snake_case							):







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None      ,	UpperCAmelCase = None      ,	UpperCAmelCase = False      ,	UpperCAmelCase = False      ,	UpperCAmelCase = None      ,	UpperCAmelCase = None      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(
					    features=UpperCAmelCase      ,	cache_dir=UpperCAmelCase      ,	keep_in_memory=UpperCAmelCase      ,	streaming=UpperCAmelCase      ,	num_proc=UpperCAmelCase      ,	**UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			Generator(
					    cache_dir=UpperCAmelCase      ,	features=UpperCAmelCase      ,	generator=UpperCAmelCase      ,	gen_kwargs=UpperCAmelCase      ,	**UpperCAmelCase      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.streaming:
						lowerCAmelCase_      							=			self.builder.as_streaming_dataset(split='''train'''   )
					# Build regular (map-style) dataset
					else:
						lowerCAmelCase_      							=			None
						lowerCAmelCase_      							=			None
						lowerCAmelCase_      							=			None
						lowerCAmelCase_      							=			None

						self.builder.download_and_prepare(
						    download_config=UpperCAmelCase      ,	download_mode=UpperCAmelCase      ,	verification_mode=UpperCAmelCase      ,	base_path=UpperCAmelCase      ,	num_proc=self.num_proc      ,	)
						lowerCAmelCase_      							=			self.builder.as_dataset(
						    split='''train'''      ,	verification_mode=UpperCAmelCase      ,	in_memory=self.keep_in_memory   )
					return dataset



"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}







class    __a						( __snake_case							):
				lowerCamelCase     :       Union[str, Any]			       ='open-llama'







				def __init__( self      ,	UpperCAmelCase=10_0000      ,	UpperCAmelCase=4096      ,	UpperCAmelCase=1_1008      ,	UpperCAmelCase=32      ,	UpperCAmelCase=32      ,	UpperCAmelCase="silu"      ,	UpperCAmelCase=2048      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=1E-6      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0      ,	UpperCAmelCase=1      ,	UpperCAmelCase=2      ,	UpperCAmelCase=False      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=None      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			vocab_size
					lowerCAmelCase_      							=			max_position_embeddings
					lowerCAmelCase_      							=			hidden_size
					lowerCAmelCase_      							=			intermediate_size
					lowerCAmelCase_      							=			num_hidden_layers
					lowerCAmelCase_      							=			num_attention_heads
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			rms_norm_eps
					lowerCAmelCase_      							=			use_cache
					lowerCAmelCase_      							=			kwargs.pop(
					    '''use_memorry_efficient_attention'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			hidden_dropout_prob
					lowerCAmelCase_      							=			attention_dropout_prob
					lowerCAmelCase_      							=			use_stable_embedding
					lowerCAmelCase_      							=			shared_input_output_embedding
					lowerCAmelCase_      							=			rope_scaling
					self._rope_scaling_validation()

					super().__init__(
					    pad_token_id=UpperCAmelCase      ,	bos_token_id=UpperCAmelCase      ,	eos_token_id=UpperCAmelCase      ,	tie_word_embeddings=UpperCAmelCase      ,	**UpperCAmelCase      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.rope_scaling is None:
						return

					if not isinstance(self.rope_scaling      ,	UpperCAmelCase   ) or len(self.rope_scaling   ) != 2:
						raise ValueError(
						    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
						    F"""got {self.rope_scaling}"""   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''type'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''factor'''      ,	UpperCAmelCase   )
					if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""   )
					if rope_scaling_factor is None or not isinstance(UpperCAmelCase      ,	UpperCAmelCase   ) or rope_scaling_factor <= 1.0:
						raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""   )